MiMo-V2.5-ASR is a state-of-the-art end-to-end automatic speech recognition (ASR) model developed by the Xiaomi MiMo team. It is built to deliver accurate and robust transcription across Mandarin Chinese and English, multiple Chinese dialects, code-switched speech, song lyrics, knowledge-intensive content, noisy acoustic environments, and multi-speaker conversations. MiMo-V2.5-ASR achieves state-of-the-art results on a wide range of public benchmarks.

Automatic speech recognition systems are expected to faithfully transcribe speech signals that originate from diverse languages, dialects, accents, and domains, and that are captured under a wide variety of acoustic conditions. While conventional end-to-end models perform well on in-domain data, they still fall short of real-world requirements in challenging scenarios such as dialect mixing, code-switching, knowledge-intensive content, noisy environments, and multi-speaker conversations. We present MiMo-V2.5-ASR, a large-scale end-to-end speech recognition model developed by the Xiaomi MiMo team. Through large-scale mid-training, high-quality supervised fine-tuning, and a novel reinforcement-learning algorithm, MiMo-V2.5-ASR achieves systematic improvements along the following dimensions:

• 🗣️ Chinese Dialects: Native support for Wu, Cantonese, Hokkien, Sichuanese, and more.
• 🔀 Code-Switch: Seamless Chinese–English code-switching transcription with no language tags required.
• 🎵 Song Recognition: High-precision lyrics transcription for Chinese and English songs, even with mixed accompaniment and vocals.
• 🔊 Noisy Environments: Robust recognition under heavy noise, far-field capture, and other adverse acoustic conditions.
• 👥 Multi-Speaker: Accurate transcription of overlapping, multi-party conversations such as meetings.
• 🇬🇧 Complex English Scenarios: Leading performance on the Open ASR Leaderboard for challenging English benchmarks such as AMI.
• 📚 Knowledge-Intensive Recognition: Precise recognition of classical poetry, technical terminology, personal names, place names, and other knowledge-dense material.
• 📝 Native Punctuation: Punctuation generated natively from prosody and semantics, delivering ready-to-use transcripts with no post-processing needed.

MiMo-V2.5-ASR has been evaluated across a broad set of benchmarks spanning standard Mandarin and English, Chinese dialects, lyric recognition, and internal business scenarios. The chart below summarizes the average performance of MiMo-V2.5-ASR across these scenarios.

For per-benchmark numbers and specific qualitative cases, please refer to our blog.

pip install huggingface-hub

hf download XiaomiMiMo/MiMo-Audio-Tokenizer --local-dir ./models/MiMo-Audio-Tokenizer
hf download XiaomiMiMo/MiMo-V2.5-ASR --local-dir ./models/MiMo-V2.5-ASR

Spin up the MiMo-V2.5-ASR demo in minutes with the built-in Gradio app.

• Python 3.12
• CUDA >= 12.0

git clone https://github.com/XiaomiMiMo/MiMo-V2.5-ASR.git
cd MiMo-V2.5-ASR
pip install -r requirements.txt
pip install flash-attn==2.7.4.post1

pip install /path/to/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp312-cp312-linux_x86_64.whl

python run_mimo_asr.py

This launches a local Gradio interface for MiMo-V2.5-ASR. You can:

• Upload an audio file or record directly from your microphone.
• Optionally specify a language tag (Chinese / English / Auto) to bias the model for a specific language, or leave it to Auto for automatic language detection (recommended for code-switched speech).
• The demo calls the asr_sft() interface under the hood.

The interface provides a Model Configuration tab for setting local model and tokenizer paths, and a Speech Recognition tab where you drop in audio, pick a language tag, and hit Transcribe — the decoded text and processing status stream into the panels on the right.

Figure: Gradio demo for MiMo-V2.5-ASR — upload an audio clip or record from your microphone, choose a language tag, and get the transcription on the right.

To load the model and tokenizer automatically at startup, pass their paths on the command line:

python run_mimo_asr.py \
    --model-path ./models/MiMo-V2.5-ASR \
    --tokenizer-path ./models/MiMo-Audio-Tokenizer

Otherwise, enter the local paths for MiMo-Audio-Tokenizer and MiMo-V2.5-ASR in the Model Configuration tab, then start transcribing!

Basic usage with the asr_sft interface:

from src.mimo_audio.mimo_audio import MimoAudio

model = MimoAudio(
    model_path="./models/MiMo-V2.5-ASR",
    tokenizer_path="./models/MiMo-Audio-Tokenizer",
)

# Automatic language detection (recommended for code-switching)
text = model.asr_sft("path/to/audio.wav")
print(text)

# With explicit language tag
text_zh = model.asr_sft("path/to/audio.wav", audio_tag="<chinese>")
text_en = model.asr_sft("path/to/audio.wav", audio_tag="<english>")

@misc{coreteam2026mimov25asr,
      title={MiMo-V2.5-ASR: Robust Speech Recognition Across Languages, Dialects, and Complex Acoustic Scenarios},
      author={LLM-Core-Team Xiaomi},
      year={2026},
      url={https://github.com/XiaomiMiMo/MiMo-V2.5-ASR},
}

Please contact us at mimo@xiaomi.com or open an issue if you have any questions.